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A prompt tuning method based on relation graphs for few-shot relation extraction.

Zirui Zhang1, Yiyu Yang2, Benhui Chen3

  • 1Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, Jiangsu, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 4, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances few-shot relation extraction by integrating global and local relation graphs into prompt-tuning. The method improves performance, especially in distinguishing similar relations with limited data.

Keywords:
Few-shotKnowledge graphPrompt tuningRelation extractionRelation graph

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Area of Science:

  • Natural Language Processing
  • Machine Learning

Background:

  • Few-shot relation extraction faces challenges with limited data and distinguishing similar relations.
  • Prompt-tuning is effective but struggles with fine-grained distinctions in low-resource settings.

Purpose of the Study:

  • To improve few-shot relation extraction by enhancing prompt-tuning with graph-based information.
  • To address the challenge of differentiating similar relations using scarce resources.

Main Methods:

  • Constructing a global relation graph to enhance sample feature representations across relations.
  • Partitioning the global graph into local relation subgraphs for intra-relation optimization.
  • Integrating semantic knowledge from relation labels into the prompt-tuning framework.

Main Results:

  • Significant performance improvements demonstrated on four low-resource datasets.
  • Enhanced ability to discern between similar relation types.
  • Improved tuning efficiency and effective utilization of limited supervised information.

Conclusions:

  • The proposed graph-enhanced prompt-tuning method effectively leverages limited data for relation extraction.
  • Integrating global and local relation graphs, along with label semantics, boosts performance and discriminative power.
  • This approach offers a robust solution for low-resource relation extraction tasks.